Artificial intelligence is no longer a speculative technology waiting somewhere beyond the horizon. It is already reshaping science, education, healthcare, labour markets, media systems and political decision-making. The real question is therefore no longer whether AI will matter, but who will have the capacity to understand, shape and govern it.
The preliminary report of the Independent International Scientific Panel on AI is valuable precisely because it avoids the usual extremes. It neither treats AI as an inevitable salvation story nor as a simple catastrophe narrative. Instead, it describes a technology whose benefits are real, whose risks are unevenly distributed, and whose governance is still far behind the speed of development.
For Aiciety, this is the central point: AI is not just a technical issue. It is a societal infrastructure question. Access, control, accountability, institutional competence and democratic oversight will determine whether AI expands human capabilities or merely concentrates power further in the hands of a few companies and states.
The report is therefore worth reading not as another abstract AI-risk document, but as an early map of the political, economic and cultural terrain on which the next phase of AI development will be fought.
Below, we document the full executive summary of the report. It is reproduced here to make the argument accessible in context: not as a final answer to the governance question, but as an important contribution to the evidence base on which future decisions about AI should rest:
Executive Summary
This document seeks to present a balanced analysis of the risks and opportunities of artificial intelligence (AI). “Balanced” means a commitment to evaluating empirical data without undue bias towards optimism or pessimism. The potential benefits of AI are enormous. Deployed and applied thoughtfully, AI can support progress towards achieving the Sustainable Development Goals, advance health science and increase access to education. At the same time, the rapid pace of technological development and the breadth of potential applications present policymakers with significant challenges. The rapid, unchecked deployment of the technology at scale also presents considerable risks, including harms to the mental health of users, potential use as a destructive tool, impacts on social, economic and environmental systems, and challenges associated with controlling the technology. This report does not aim to consider the full scope of all possible opportunities and risks but rather focuses on some of the most pressing ones.
Capabilities and adoption
Recent years have seen rapid, and in some areas accelerating, progress in a range of AI capabilities. Significant investments in computing power, new AI methodologies, and specialized training data have led to sustained improvements in a wide range of AI capabilities. These include fluent conversation, functional code generation, expert-level reasoning in mathematics and science, large-scale data analysis, and the generation of image, audio and video content. Limitations remain, such as in reliability, obtaining strong performance across human languages and cultures, interacting with physical systems, executing complex or multi-step projects and producing factual outputs; in general, however, technical progress in many important domains has proceeded quickly, beyond the typical expectation of technology advancement, for several years now.
These gains have unlocked useful applications across science, health, agriculture, accessibility, knowledge work and information technology, including in the development of AI itself. For example, in science, AlphaFold has predicted the structures of more than 200 million proteins, now used by over 3 million researchers, and accelerated drug design, vaccine development and antibiotic resistance research. Radiologists have also used AI to detect breast cancer earlier, while front-line health workers in low-resource settings use AI tools adapted to local languages to deliver better-quality healthcare services.
AI adoption has accelerated broadly, and unevenly, across countries and sectors. Globally, over a billion people now use conversational AI weekly. Yet AI access and usage vary widely globally, with adoption across the global South lagging far behind the global North. Furthermore, there are significant differences in compute infrastructure and models between advanced economies. This disparity reflects, and may even reinforce, existing inequalities. AI development itself is even more concentrated: according to recent estimates, the United States of America accounts for 75% of the computing power among the world’s top 500 AI supercomputers, with China accounting for 15%. Companies in the United States and China also develop almost all leading general-purpose models, and a small number of countries control critical inputs for the supply chain of AI computer chips.
While the shift towards AI agents is under way, their future adoption and economic impacts will likely be shaped by continued improvements in their ability to accomplish knowledge work with little or no human oversight. An AI agent is a computer system that can plan and autonomously act towards achieving goals, using the tools at its disposal. These systems have been improving rapidly in recent years, with one study finding that the length of certain software tasks that leading systems can accomplish has been doubling every four to seven months. If this rate of improvement continues, AI agents will soon complete tasks that currently take human programmers days or weeks. Because they can work with little oversight and at rapid speed, AI agents may lead to significant economic and scientific benefits. For example, agentic AI systems in self-driving chemistry labs have demonstrated more than a tenfold increase in the speed of materials discovery. AI-assisted literature screening may have reduced workloads by roughly 60% in some research settings. AI agents therefore carry significant implications across all industries. At the same time, their deployment raises urgent questions for labour markets, cybersecurity, the information ecosystem, and the governance and controllability of future AI systems.
Understanding and managing risks
AI development entails risks, with potential negative impacts on human rights, social systems and the environment. For example, AI-generated child sexual abuse material and deepfake-enabled sexual violence now circulate more frequently on the Internet, disproportionately harming women and children. Sycophantic AI behaviour, where AI responses reinforce users’ existing beliefs regardless of accuracy, has been linked to several severe mental health incidents, including documented deaths. AI makes it easier to produce and target persuasive content at scale, including content designed to mislead, contributing to a gradual erosion of information integrity that can weaken the shared reality required for public trust, social cohesion and democratic deliberation. Criminals and bad actors have been documented using AI systems to assist in cyberattacks. Many of these harms fall disproportionately on already disadvantaged populations.
Looking ahead, the gap between rapidly improving capabilities and effective risk management methods may lead to catastrophic outcomes. For example, advanced technical abilities may allow novice private actors to use AI in malicious ways across a range of applications such as fraud, social engineering, cybersecurity, disinformation, biotechnology and financial manipulation. Reliable methods for retaining control over highly autonomous AI systems are lacking. There are no scientific guarantees that AI agents will not violate instructions, and evidence is accumulating of cases where they already violate them. In laboratory settings, AI systems have been shown to violate their safety instructions to avoid being shut down. Similar behaviour may pose challenges to evaluation and oversight methods, as the ability of leading AI systems to recognize testing environments and produce misleading evaluation results that would favour their continued operation grows. Additionally, novel risks may arise from interactions between multiple agents.
AI risks are unevenly distributed across populations and countries, while AI development and the wealth it creates are highly concentrated. The concentration of AI capabilities in a small number of firms and countries could enable authoritarian capture and undermine democratic accountability.
Governing artificial intelligence to unlock benefits and mitigate risks
Realizing the full benefits of AI while minimizing its risks requires good governance. Economic and labour gains and their equitable distribution are not automatic: with complementary investments in skills, workflows, infrastructure and labour-market institutions, technology can create new jobs that do not exist right now — over 60% of jobs in 2018 compared to 1945 are new. Without these investments, AI risks widening inequality, displacing workers and shifting wealth from labour to capital rather than creating sustainable good jobs — those with fair compensation, worker autonomy and a reliable path to social dignity. AI can profoundly expand human capabilities through personalized education, accessible mental health tools and improved assistive technologies, but realizing these opportunities safely requires dedicated investments and policies to incentivize equitable access and reward innovation, while preventing the exploitation of vulnerable populations, particularly children, and avoiding displacement of expertise, psychological dependency or cultural and linguistic erasure.
Policymakers seeking to shape this governance face an evidence dilemma: they need evidence to make informed consequential governance decisions, but by the time the evidence exists, it might be too late to make them, as the evidence lags behind the pace of AI development. Dozens of distinct governance instruments that seek to embed ethics and human rights in AI systems are already in use across jurisdictions, but they are fragmented, are concentrated among a few corporations and rarely measure real-world effectiveness. Evaluation methods themselves are underdeveloped, and the institutions needed to provide independent capability and risk assessments remain embryonic.
The capacity to act on existing evidence of AI risks and impacts is unevenly distributed. Most countries, including many advanced economies, lack the technical expertise to assess the most capable “frontier” models or to participate meaningfully in their governance. Compute infrastructure, evaluation expertise and data, for example to cover different languages, are concentrated where AI is built, leaving most Member States dependent on systems they cannot build, inspect, audit or fully adapt to local context. Access to AI tools alone does not produce equal benefit; the complementary investments in data, skills, workflows and institutions that turn access into useful, cost-effective and safe deployment are necessary yet unequally distributed.
Concrete next steps to close the above gaps exist, but each requires sustained investment in Member State capacity to shape, evaluate and deploy AI. This preliminary report is itself part of the Panel’s contribution, a shared evidence base for Member States navigating increasingly urgent decisions. As the Panel’s understanding deepens through continued engagement with Member States and the broader scientific community, so too will its analysis, expanding beyond the gaps identified to chart the trajectories, tensions and opportunities that will define the future of AI.
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